Operations Research, Systems Engineering and Industrial Engineering Commons^™

An Agent-Based Approach To Human Migration Movement, Larry Lin, Kathleen M. Carley, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale.

Traffic Simulation Model For Port Planning And Congestion Prevention, Baoxiang Li, Kar Way Tan, Trong Khiem Tran

Research Collection School Of Computing and Information Systems

Effective management of land-side transportation provides the competitive advantage to port terminal operators in improving services and efficient use of limited space in an urban port. We present a hybrid simulation model that combines traffic-flow modeling and discrete-event simulation for land-side port planning and evaluation of traffic conditions for a number of what-if scenarios. We design our model based on a real-world case of a bulk cargo port. The problem is interesting due to complexity of heterogeneous closed-looped internal vehicles and external vehicles traveling in spaces with very limited traffic regulation (no traffic lights, no traffic wardens) and the traffic …

Using Timing-Based Side Channels For Anomaly Detection In Industrial Control Systems, Stephen Dunlap, Jonathan W. Butts, Juan L. Lopez Jr., Mason J. Rice, Barry E. Mullins

Faculty Publications

The critical infrastructure, which includes the electric power grid, railroads and water treatment facilities, is dependent on the proper operation of industrial control systems. However, malware such as Stuxnet has demonstrated the ability to alter industrial control system parameters to create physical effects. Of particular concern is malware that targets embedded devices that monitor and control system functionality, while masking the actions from plant operators and security analysts. Indeed, system security relies on guarantees that the assurance of these devices can be maintained throughout their lifetimes. This paper presents a novel approach that uses timing-based side channel analysis to establish …

Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their …

Improving Carbon Efficiency Through Container Size Optimization And Shipment Consolidation, Nang Laik Ma, Kar Way Tan, Edwin Lik Ming Chong

Research Collection School Of Computing and Information Systems

Purpose: Many manufacturing companies that ship goods through full container loads found themselves under-utilizing the containers and resulting in higher carbon footprint per volume shipment. One of the reasons is the choice of non-ideal container sizes for their shipments. Consolidation fills up the containers more efficiently that reduces the overall carbon footprint. The objective of this paper is to support decisions on selection of appropriate combination of container sizes and shipment consolidation for a manufacturing company. We develop two-steps model which first takes the volumes to be shipped as an input and provide the combination of container sizes required; then …

Enhancing Local Search With Adaptive Operator Ordering And Its Application To The Time Dependent Orienteering Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

No abstract provided.

A Hybrid Tabu/Scatter Search Algorithm For Simulation-Based Optimization Of Multi-Objective Runway Operations Scheduling, Bulent Soykan

Engineering Management & Systems Engineering Theses & Dissertations

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class …

Oversampling Methods For Imbalanced Dataset Classification And Their Application To Gynecological Disorder Diagnosis, Iman Nekooeimehr

USF Tampa Graduate Theses and Dissertations

In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to some of the classes (majority classes), while only a few instances are from the other classes (minority classes). Conventional classifiers will strongly favor the majority class and ignore the minority instances. The imbalance problem can occur in both binary data classification and also in ordinal regression. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. Extensive research has been performed for addressing imbalanced datasets for binary classification; however, current methods do not address …

Analysis Of A Parallel Machine Scheduling Problem With Sequence Dependent Setup Times And Job Availability Intervals, Ridvan Gedik, Chase Rainwater, Heather Nachtmann, Edward A. Pohl

Mechanical and Industrial Engineering Faculty Publications

In this study, we propose constraint programming (CP) model and logic-based Benders algorithms in order to make the best decisions for scheduling non-identical jobs with availability intervals and sequence dependent setup times on unrelated parallel machines in a fixed planning horizon. In this problem, each job has a profit, cost and must be assigned to at most one machine in such a way that total profit is maximized. In addition, the total cost has to be less than or equal to a budget level. Computational tests are performed on a real-life case study prepared in collaboration with the U.S. Army …

Designing And Comparing Multiple Portfolios Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir

Research Collection School Of Computing and Information Systems

Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …

Strategic Planning For Setting Up Base Stations In Emergency Medical Systems, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Emergency Medical Systems (EMSs) are an important component of public health-care services. Improving infrastructure for EMS and specifically the construction of base stations at the ”right” locations to reduce response times is the main focus of this paper. This is a computationally challenging task because of the: (a) exponentially large action space arising from having to consider combinations of potential base locations, which themselves can be significant; and (b) direct impact on the performance of the ambulance allocation problem, where we decide allocation of ambulances to bases. We present an incremental greedy approach to discover the placement of bases that …

Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …

Dual Formulations For Optimizing Dec-Pomdp Controllers, Akshat Kumar, Hala Mostafa, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDP is an expressive model for multi-agent planning. Finite-state controllers (FSCs)---often used to represent policies for infinite-horizon problems---offer a compact, simple-to-execute policy representation. We exploit novel connections between optimizing decentralized FSCs and the dual linear program for MDPs. Consequently, we describe a dual mixed integer linear program (MIP) for optimizing deterministic FSCs. We exploit the Dec-POMDP structure to devise a compact MIP and formulate constraints that result in policies executable in partially-observable decentralized settings. We show analytically that the dual formulation can also be exploited within the expectation maximization (EM) framework to optimize stochastic FSCs. The resulting EM algorithm …

Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Influence Maximization is the problem of finding a fixed size set of nodes, which will maximize the expected number of influenced nodes in a social network. The number of influenced nodes is dependent on the influence strength of edges that can be very noisy. The noise in the influence strengths can be modeled using a random noise or adversarial noise model. It has been shown that all random processes that independently affect edges of the graph can be absorbed into the activation probabilities themselves and hence random noise can be captured within the independent cascade model. On the other hand, …

Reinforcement Learning Framework For Modeling Spatial Sequential Decisions Under Uncertainty: (Extended Abstract), Truc Viet Le, Siyuan Liu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider the problem of trajectory prediction, where a trajectory is an ordered sequence of location visits and corresponding timestamps. The problem arises when an agent makes sequential decisions to visit a set of spatial locations of interest. Each location bears a stochastic utility and the agent has a limited budget to spend. Given the agent's observed partial trajectory, our goal is to predict the remaining trajectory. We propose a solution framework to the problem considering both the uncertainty of utility and the budget constraint. We use reinforcement learning (RL) to model the underlying decision processes and inverse RL to …

Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We study the problem of optimizing the trajectories of agents moving over a network given their preferences over which nodes to visit subject to operational constraints on the network. In our running example, a theme park manager optimizes which attractions to include in a day-pass to maximize the pass’s appeal to visitors while keeping operational costs within budget. The first challenge in this combinatorial optimization problem is that it involves quantities (expected visit frequencies of each attraction) that cannot be expressed analytically, for which we use the Sample Average Approximation. The second challenge is that while sampling is typically done …

Approximate Inference Using Dc Programming For Collective Graphical Models, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

Research Collection School Of Computing and Information Systems

Collective graphical models (CGMs) provide a framework for reasoning about a population of independent and identically distributed individuals when only noisy and aggregate observations are given. Previous approaches for inference in CGMs work on a junction-tree representation, thereby highly limiting their scalability. To remedy this, we show how the Bethe entropy approximation naturally arises for the inference problem in CGMs. We reformulate the resulting optimization problem as a difference-of-convex functions program that can capture different types of CGM noise models. Using the concave-convex procedure, we then develop a scalable message-passing algorithm. Empirically, our approach is highly scalable and accurate for …

In The Face Of Anticipation: Decision Making Under Visible Uncertainty As Present In The Safest-With-Sight Problem, Bryan A. Knowles

Masters Theses & Specialist Projects

Pathfinding, as a process of selecting a fixed route, has long been studied in

Computer Science and Mathematics. Decision making, as a similar, but intrinsically different, process of determining a control policy, is much less studied. Here, I propose a problem that appears to be of the first class, which would suggest that it is easily solvable with a modern machine, but that would be too easy, it turns out. By allowing a pathfinding to anticipate and respond to information, without setting restrictions

on the \structure" of this anticipation, selecting the \best step" appears to be an intractable problem.

After …

Feature Knowledge Based Fault Detection Of Induction Motors Through The Analysis Of Stator Current Data, Ting Yang, Haibo Pen, Zhaoxia Wang, Che Sau Chang

Research Collection School Of Computing and Information Systems

The fault detection of electrical or mechanical anomalies in induction motors has been a challenging problem for researchers over decades to ensure the safety and economic operations of industrial processes. To address this issue, this paper studies the stator current data obtained from inverter-fed laboratory induction motors and investigates the unique signatures of the healthy and faulty motors with the aim of developing knowledge based fault detection method for performing online detection of motor fault problems, such as broken-rotor-bar and bearing faults. Stator current data collected from induction motors were analyzed by leveraging fast Fourier transform (FFT), and the FFT …

Harmonic Analysis In Integrated Energy System Based On Compressed Sensing, Ting Yang, Haibo Pen, Dan Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The advent of Integrated Energy Systems enabled various distributed energy to access the system through different power electronic devices. The development of this has made the harmonic environment more complex. It needs low complexity and high precision of harmonic detection and analysis methods to improve power quality. To solve the shortages of large data storage capacities and high complexity of compression in sampling under the Nyquist sampling framework, this research paper presents a harmonic analysis scheme based on compressed sensing theory. The proposed scheme enables the performance of the functions of compressive sampling, signal reconstruction and harmonic detection simultaneously. In …

Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar

Research Collection School Of Computing and Information Systems

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …

An Efficient Solution To The Mixed Shop Scheduling Problem Using A Modified Genetic Algorithm, V. Nguyen, H. P. Bao

Mechanical & Aerospace Engineering Faculty Publications

The mixed job shop scheduling problem is one in which some jobs have fixed machine orders and other jobs may be processed in arbitrary orders. In past literature, optimal solutions have been proposed based on adaptations of classical solutions such as by Johnson, Thompson and Giffler among many others, by pseudopolynomial algorithms, by simulation, and by Genetic Algorithms (GA). GA based solutions have been proposed for flexible Job shops. This paper proposes a GA algorithm for the mixed job shop scheduling problem. The paper starts with an analysis of the characteristics of the so-called mixed shop problem. Based on those …

Performance Based Contracting For The Manufacturing Industry By Using Integrated Platform And Dynamic Pricing Model, Lindawati, Aldy Gunawan

Research Collection Lee Kong Chian School Of Business

Although Performance Based Contracting (PBC) concept is not totally new, the PBC adaptation in Industrial Machinery and Components (IMC) manufacturing, which produces high-value and long life machineries, is rather slow and it is done with extra caution. Three main concerns for manufacturers to implement PBC are the investment cost, the maintenance cost and possible revenue loss. To handle these concerns and accelerate the PBC implementation, we propose an integrated platform that consists of three components: dynamic pricing, sensor data feeding and machinery monitoring. We model the dynamic pricing as an optimization problem and propose Genetic Algorithm to solve the problem. …

The Reconfigurable Machinery Efficient Workspace Analysis Based On The Twist Angles, Ana M. Djuric, Vukica Jovanovic, Mirjana Filipovic, Ljubinko Kevac

Engineering Technology Faculty Publications

A novel methodology for the calculation, visualisation and analysis of the Reconfigurable Machinery Efficient Workspace (RMEW), based on the twist angles, is presented in this paper. The machinery's kinematic parameters are used for calculating the workspace, while the efficient workspace is associated with the machinery's path and includes the end-effector position and orientation. To analyse and visualise many different machinery efficient workspaces at the same time, the calculation is based on the previously developed and validated complex reconfigurable machinery's kinematic structure named n-DOF Global Kinematic Model (n-GKM). An industrial robot is used as an example to demonstrate …

Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

The resource-constrained project scheduling problem with minimum and maximum time lags (RCPSP/max) is a general model for resource scheduling in many real-world problems (such as manufacturing and construction engineering). We consider RCPSP/max problems where the durations of activities are stochastic and resources can have unforeseen breakdowns. Given a level of allowable risk, (Formula presented.), our mechanisms aim to compute the minimum robust makespan execution strategy. Robust makespan for an execution strategy is any makespan value that has a risk less than (Formula presented.). The risk for a makespan value, (Formula presented.) given an execution strategy, is the probability that a …

Spatiotemporal Sensing And Informatics For Complex Systems Monitoring, Fault Identification And Root Cause Diagnostics, Gang Liu

USF Tampa Graduate Theses and Dissertations

In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of sensor networks and data acquisition systems to increase information visibility. Multi-sensor systems bring the proliferation of high-dimensional functional Big Data that capture rich information on the evolving dynamics of natural and engineered processes. With spatially and temporally dense data readily available, there is an urgent need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the big data communicated by the contemporary complex systems, (ii) the extraction and identification of pertinent knowledge about the environmental …

Designing Bus Transit Services For Routine Crowd Situations At Large Event Venues, Jianli Du, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We are concerned with the routine crowd management problem after a major event at a known venue. Without properly design complementary transport services, such sudden crowd build-ups will overwhelm the existing infrastructure. In this paper, we introduce a novel flow-rate based model to model the dynamic movement of passengers over the transportation flow network. Based on this basic model, an integer linear programming model is proposed to solve the bus transit problem permanently. We validate our model against a real scenario in Singapore, where a newly constructed mega-stadium hosts various large events regularly. The results show that the proposed approach …

Sails: Hybrid Algorithm For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

The Team Orienteering Problem with Time Windows (TOPTW) is the extended version of the Orienteering Problem where each node is limited by a given time window. The objective is to maximize the total collected score from a certain number of paths. In this paper, a hybridization of Simulated Annealing and Iterated Local Search, namely SAILS, is proposed to solve the TOPTW. The efficacy of the proposed algorithm is tested using benchmark instances. The results show that the proposed algorithm is competitive with the state-of-the-art algorithms in the literature. SAILS is able to improve the best known solutions for 19 benchmark …

Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu

Research Collection School Of Computing and Information Systems

As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …

Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …